{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "#import some necessary librairies\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt  # Matlab-style plotting\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "def ignore_warn(*args, **kwargs):\n",
    "    pass\n",
    "warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics\n",
    "\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "1.4.0\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import d2l as d2l\n",
    "print(torch.__version__)\n",
    "torch.set_default_tensor_type(torch.FloatTensor)\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import  utils"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "situ max value:138.0469971,seawifs max value:180.867205\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data = pd.read_csv(r'E:\\02Projects\\papers\\chlmodel\\data\\seawifs\\03 augment\\train_dataset-aug-features.csv')\n",
    "all_data.head(10)\n",
    "\n",
    "plt.figure(1,figsize=(6,6))\n",
    "plt.xlabel('pre values')\n",
    "plt.ylabel('real values')\n",
    "plt.xlim([0,25])\n",
    "plt.ylim([0,25])\n",
    "plt.scatter(all_data.in_situ_chl,all_data.seawifs_chlor_a)\n",
    "print(\"situ max value:{},seawifs max value:{}\".format(all_data.in_situ_chl.max(),all_data.seawifs_chlor_a.max()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    " ###  按照比例分配训练和测试数据"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "percent = 0.8 # 80% 的数据将用于训练，20%的数据用于测试\n",
    "\n",
    "all_features =pd.DataFrame(data=None, columns=(all_data.iloc[:,1:-2]).columns)\n",
    "\n",
    "\n",
    "X_train_g = None;\n",
    "X_test_g = None;\n",
    "y_train_g = None;\n",
    "y_test_g = None;\n",
    "\n",
    "feature_num = -2\n",
    "\n",
    "feature_cols = None\n",
    "# 数据等值间隔\n",
    "gapValList = [0,0.04,0.08,0.16,0.32,0.64,1.28,2.56,5.12,10.24,20.48, 40.96,1000]\n",
    "for row in range(0,4):\n",
    "    for col in range(0,3):\n",
    "        idx = row*3 + col\n",
    "        curCHL = all_data[(all_data.in_situ_chl < gapValList[idx+1]) & (all_data.in_situ_chl > gapValList[idx])]\n",
    "        X,y = curCHL.iloc[:,1:feature_num].values,curCHL.iloc[:,-2:-1].values\n",
    "        feature_cols = curCHL.iloc[:,1:feature_num].columns\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "        if(X_train_g is None):\n",
    "            X_train_g = np.array(X_train)\n",
    "            X_test_g = np.array(X_test)\n",
    "            y_train_g = np.array(y_train)\n",
    "            y_test_g = np.array(y_test)\n",
    "        else:\n",
    "            X_train_g = np.append(X_train_g,X_train,axis=0)\n",
    "            X_test_g = np.append(X_test_g,X_test,axis=0)\n",
    "            y_train_g = np.append(y_train_g,y_train,axis=0)\n",
    "            y_test_g = np.append(y_test_g,y_test,axis=0)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "===========================\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# 构建标准化scaler\n",
    "X_All = np.append(X_train_g,X_test_g,axis=0)\n",
    "import sklearn.preprocessing as preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(X_All)\n",
    "\n",
    "print(\"===========================\")\n",
    "# 执行标准化\n",
    "X_train_g = scaler.transform(X_train_g)\n",
    "X_test_copied = X_test_g\n",
    "X_test_g = scaler.transform(X_test_g)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "cols = feature_cols\n",
    "all_features_analy = pd.DataFrame(data=X_train_g,columns=cols)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "rrs412           0\nrrs443           0\nrrs490           0\nrrs510           0\nrrs555           0\nrrs670           0\nrrs412/rrs443    0\nrrs412/rrs490    0\nrrs412/rrs510    0\nrrs412/rrs555    0\nrrs412/rrs670    0\nrrs443/rrs412    0\nrrs443/rrs490    0\nrrs443/rrs510    0\nrrs443/rrs555    0\nrrs443/rrs670    0\nrrs490/rrs412    0\nrrs490/rrs443    0\nrrs490/rrs510    0\nrrs490/rrs555    0\nrrs490/rrs670    0\nrrs510/rrs412    0\nrrs510/rrs443    0\nrrs510/rrs490    0\nrrs510/rrs555    0\nrrs510/rrs670    0\nrrs555/rrs412    0\nrrs555/rrs443    0\nrrs555/rrs490    0\nrrs555/rrs510    0\nrrs555/rrs670    0\nrrs670/rrs412    0\nrrs670/rrs443    0\nrrs670/rrs490    0\nrrs670/rrs510    0\nrrs670/rrs555    0\ndtype: int64"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 8
    }
   ],
   "source": [
    "test = all_features_analy.apply(lambda x : np.isinf(x))\n",
    "test.sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [Missing Ratio]\nIndex: []",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Missing Ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 9
    }
   ],
   "source": [
    "all_data_na = (all_features_analy.isnull().sum() / len(all_features_analy)) * 100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\n",
    "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
    "missing_data.head(20)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = all_features_analy.corr()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True)\n",
    "\n",
    "plt.savefig('feature-augments.png',dpi=300)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 建模\n",
    "--- \n",
    "引入依赖包"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC,LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "y_train = all_data.iloc[:,-1]\n",
    "#Validation function\n",
    "n_folds = 5\n",
    "\n",
    "def rmsle_cv(model):\n",
    "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(all_features_analy.values)\n",
    "    rmse= np.sqrt(-cross_val_score(model, all_features_analy.values, y_train_g, scoring=\"neg_mean_squared_error\", cv = kf))\n",
    "    return(rmse)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## LASSO Regression "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "lasso =  Lasso(alpha =0.0005, random_state=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Elastic Net Regression \n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "ENet =ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Kernel Ridge Regression :\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Gradient Boosting Regression :\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [
    "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =5)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## XGBoost :"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "                             learning_rate=0.05, max_depth=3, \n",
    "                             min_child_weight=1.7817, n_estimators=2200,\n",
    "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "                             subsample=0.5213, silent=1,\n",
    "                             random_state =7, nthread = -1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## LightGBM \n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
    "                              learning_rate=0.05, n_estimators=720,\n",
    "                              max_bin = 55, bagging_fraction = 0.8,\n",
    "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
    "                              feature_fraction_seed=9, bagging_seed=9,\n",
    "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## RandomForestRegressor "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.9798915853377834,\nmean absolute error:0.1687348559242762,\nmean squared error:0.30969405141892076,\nmean squared log error:0.0021018964410033218 \nmedian_absolute_error:0.02395555000000016, \nr2Score:0.9798637183628203\n[1.29313538e-04 1.89161718e-04 4.20736229e-03 6.77945323e-03\n 2.06199436e-03 2.86047162e-03 3.41491969e-05 2.40203386e-05\n 1.60575825e-05 1.81414167e-05 1.81718705e-05 6.17149136e-05\n 8.21706897e-04 1.09615728e-04 3.55813977e-04 2.37793590e-04\n 2.32311959e-05 3.11600210e-04 1.04795084e-02 5.48937089e-02\n 3.07566354e-03 2.00314308e-05 5.16493112e-04 2.49679449e-02\n 2.61951824e-01 2.95722438e-03 2.59344711e-05 1.22506531e-03\n 3.26799604e-01 2.80572765e-01 5.69282150e-03 6.37097243e-05\n 7.40491071e-04 4.41044827e-03 1.91438703e-03 1.43260195e-03]\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model_rfr=RandomForestRegressor()\n",
    "model_rfr.fit(X_train_g,y_train_g)\n",
    "predictVal = model_rfr.predict(X_test_g)\n",
    "y_test_t = y_test_g.flatten()\n",
    "utils.plotPicturesEx(y_test_t,predictVal,'随机森林')\n",
    "utils.accuracyAssessment(y_test_g,predictVal)\n",
    "print(model_rfr.feature_importances_)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Base models scores"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "\nLasso score: 4.8658 (3.8980)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(lasso)\n",
    "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "ElasticNet score: 4.4447 (3.1342)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(ENet)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Kernel Ridge score: 4.8999 (2.4089)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(KRR)\n",
    "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Gradient Boosting score: 4.4776 (5.8613)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(GBoost)\n",
    "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Xgboost score: 4.3717 (6.1524)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(model_xgb)\n",
    "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "LGBM score: 4.9751 (4.4990)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(model_lgb)\n",
    "print(\"LGBM score: {:.4f} ({:.4f})\\n\" .format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "rfr score: 2.1644 (2.9311)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "score = rmsle_cv(model_rfr)\n",
    "print(\"rfr score: {:.4f} ({:.4f})\\n\" .format(score.mean(), score.std()))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Averaged base models class"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [],
   "source": [
    "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self, models):\n",
    "        self.models = models\n",
    "        \n",
    "    # we define clones of the original models to fit the data in\n",
    "    def fit(self, X, y):\n",
    "        self.models_ = [clone(x) for x in self.models]\n",
    "        \n",
    "        # Train cloned base models\n",
    "        for model in self.models_:\n",
    "            model.fit(X, y)\n",
    "\n",
    "        return self\n",
    "    \n",
    "    #Now we do the predictions for cloned models and average them\n",
    "    def predict(self, X):\n",
    "        predictions = np.column_stack([\n",
    "            model.predict(X) for model in self.models_\n",
    "        ])\n",
    "        return np.mean(predictions, axis=1)   "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      " Averaged base models score: 3.5795 (2.9957)\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso,model_rfr))\n",
    "\n",
    "score = rmsle_cv(averaged_models)\n",
    "print(\" Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Stacking averaged Models Class\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    def __init__(self, base_models, meta_model, n_folds=5):\n",
    "        self.base_models = base_models\n",
    "        self.meta_model = meta_model\n",
    "        self.n_folds = n_folds\n",
    "   \n",
    "    # We again fit the data on clones of the original models\n",
    "    def fit(self, X, y):\n",
    "        self.base_models_ = [list() for x in self.base_models]\n",
    "        self.meta_model_ = clone(self.meta_model)\n",
    "        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n",
    "        \n",
    "        # Train cloned base models then create out-of-fold predictions\n",
    "        # that are needed to train the cloned meta-model\n",
    "        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n",
    "        for i, model in enumerate(self.base_models):\n",
    "            for train_index, holdout_index in kfold.split(X, y):\n",
    "                instance = clone(model)\n",
    "                self.base_models_[i].append(instance)\n",
    "                instance.fit(X[train_index], y[train_index])\n",
    "                y_pred = instance.predict(X[holdout_index]).flatten()\n",
    "                print(\"y_pred:\\t{}\\t predic:\\t{} type:\\t{}\".format(y_pred.shape,out_of_fold_predictions[holdout_index, i].shape,type(y_pred)))\n",
    "                out_of_fold_predictions[holdout_index, i] = y_pred\n",
    "        \n",
    "        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n",
    "        self.meta_model_.fit(out_of_fold_predictions, y)\n",
    "        return self\n",
    "   \n",
    "    #Do the predictions of all base models on the test data and use the averaged predictions as \n",
    "    #meta-features for the final prediction which is done by the meta-model\n",
    "    def predict(self, X):\n",
    "        meta_features = np.column_stack([\n",
    "            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n",
    "            for base_models in self.base_models_ ]) \n",
    "        return self.meta_model_.predict(meta_features)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "def model_accuracy_plot(model,title='default'):\n",
    "    model.fit(all_features_analy.values, y_train_g.reshape(-1,1))\n",
    "    y_pred = model.predict(X_test_g)\n",
    "    y_pred = np.maximum(y_pred, 0)\n",
    "    import utils\n",
    "    utils.plotPicturesEx(y_test_g.flatten(),y_pred.flatten(),defaultTitle=title)\n",
    "    utils.accuracyAssessment(y_test_g,y_pred)\n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [
    "stacked_averaged_models = StackingAveragedModels(base_models = (model_rfr),\n",
    "                                                 meta_model = model_rfr )\n",
    "\n",
    "# score = rmsle_cv(stacked_averaged_models)\n",
    "# print(\"Stacking Averaged models score: {:.4f} ({:.4f})\".format(score.mean(), score.std()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Ensembling StackedRegressor, XGBoost and LightGBM\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## single model accuracy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "name": "stdout",
     "text": [
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      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
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      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>",
      "\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
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      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>",
      "\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>",
      "\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>",
      "\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\n",
      "y_pred:\t(355,)\t predic:\t(355,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\ny_pred:\t(354,)\t predic:\t(354,) type:\t<class 'numpy.ndarray'>\n",
      "explained variance score:0.9313064061787943,\nmean absolute error:0.24400655862758514,\nmean squared error:1.0585627759229834,\nmean squared log error:0.0060358913114632075 \nmedian_absolute_error:0.03309276, \nr2Score:0.9311723357650591\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(stacked_averaged_models,'堆栈 stacking generalazition')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.9787682006275826,\nmean absolute error:0.1596750398663698,\nmean squared error:0.3268970948347613,\nmean squared log error:0.0019827675544253857 \nmedian_absolute_error:0.02511737000000036, \nr2Score:0.9787451779011909\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(model_rfr,'随机森林回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.9216338444659129,\nmean absolute error:0.3255063272497904,\nmean squared error:1.2126871393545928,\nmean squared log error:0.011516798987408853 \nmedian_absolute_error:0.056674510604858286, \nr2Score:0.9211511823880707\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(model_xgb,'XGB回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.9225863214346045,\nmean absolute error:0.22068399932046054,\nmean squared error:1.1951098915791911,\nmean squared log error:0.0028574950953784645 \nmedian_absolute_error:0.028153983958723705, \nr2Score:0.922294053586243\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(GBoost,'GBoost 回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.8930971954869721,\nmean absolute error:0.8403865462547917,\nmean squared error:1.6937920970955966,\nmean squared log error:0.1563586875022338 \nmedian_absolute_error:0.5800895337482948, \nr2Score:0.889869777783331\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(lasso,'lasso 回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.8891352143069465,\nmean absolute error:0.8515303728926295,\nmean squared error:1.7529173003604315,\nmean squared log error:0.1568137343651584 \nmedian_absolute_error:0.5725043075935116, \nr2Score:0.8860254619518146\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(ENet,'ENet 回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.9408604871324953,\nmean absolute error:0.4656104504021501,\nmean squared error:0.912664378708917,\nmean squared log error:0.034044297203476064 \nmedian_absolute_error:0.22359577558174396, \nr2Score:0.9406586374981898\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracy_plot(KRR,'KRR 回归')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "RMSLE score on train data:\n2.2451942959924027\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "'''RMSE on the entire Train data when averaging'''\n",
    "\n",
    "print('RMSLE score on train data:')\n",
    "print(rmsle(y_train_g,stacked_train_pred*0.70 +\n",
    "               xgb_train_pred*0.15 + lgb_train_pred*0.15 ))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "ensemble = stacked_pred*0.70 + xgb_pred*0.15 + lgb_pred*0.15"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "53.26145074186749"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 38
    }
   ],
   "source": [
    "np.max(ensemble)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "explained variance score:0.8640852003534075,\nmean absolute error:0.47691633889316937,\nmean squared error:2.1298529500321264,\nmean squared log error:0.02561508135287987 \nmedian_absolute_error:0.209270676041871, \nr2Score:0.861517137151557\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "(0.8640852003534075,\n 0.47691633889316937,\n 2.1298529500321264,\n 0.02561508135287987,\n 0.209270676041871,\n 0.861517137151557)"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 39
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import utils\n",
    "utils.plotPicturesEx(y_test_g.flatten(),ensemble,defaultTitle='total stacking generalazition')\n",
    "utils.accuracyAssessment(y_test_g,ensemble)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}